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Pipeline run

92062496-bbec-43d1-b056-6b5e6b0f5235

Pipeline LLM cost (USD)
API 1: $0.0102 API 2: $0.0016 API 3: $0.0000 Total: $0.0118

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · AI and Data Science Strategy & Delivery
Leads AI/data science strategy and runs projects end to end, building and tuning predictive ML/deep learning models and data pipelines, then presenting insights to leadership and improving governance, quality, and scalability.
"Manage the end-to-end lifecycle of AI and data science projects, from conceptualization to deployment."
Tech stack maturity
Mainstream Modern
The skill set centers on widely used ML and GenAI tooling like Python, PyTorch, TensorFlow, scikit-learn, and embeddings, which aligns with a mainstream modern stack rather than legacy or bleeding-edge-only technologies.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
3.20 / 5
Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): Transformers, embeddings, diffusion models, multi-agent, multimodal, AI, ML, AI/ML, Generative AI, Machine Learning, Deep Learning
Evidence — skills matched in JD (35)
Python R Scala TensorFlow PyTorch Pandas Scikit-learn Natural Language Processing Machine Learning Deep Learning Data Pipelines Data Preprocessing Feature Engineering Model Selection Hyperparameter Tuning Statistical Analysis Data Mining Data Governance Data Quality Data Management Data Privacy Regulations Ethical Standards Data Infrastructure Metrics Generative AI +10
Skill cluster (6 dimension groups, role-scoped)
ML Frameworks and Libraries
PyTorch Embeddings
AI Governance and Model Security
Machine Learning
Experiment Tracking and Evaluation
Metrics
Programming Languages for ML Systems
Scala
Python Programming
Python
Cross-cutting / unaligned
R TensorFlow Pandas Scikit-learn Natural Language Processing Deep Learning Data Pipelines Data Preprocessing Feature Engineering Model Selection Hyperparameter Tuning Statistical Analysis Data Mining Data Governance Data Quality Data Management Data Privacy Regulations Ethical Standards Data Infrastructure Generative AI Transformers Diffusion Models Multimodal Agentive Workflow Multi-Agent Sync Encoders Decoders Cloud Platforms Big Data
Show KRA description ↓
• Lead the design and implementation of AI and data science strategies aligned with the company’s business objectives. • Identify opportunities for AI-driven innovations and data-driven insights across the organization. • Collaborate with senior management to align AI and data initiatives with long-term business goals. • Manage the end-to-end lifecycle of AI and data science projects, from conceptualization to deployment. • Oversee the development of predictive models, machine learning algorithms, and advanced analytics solutions. • Ensure that all AI and data science projects meet quality, accuracy, and scalability standards. • Design and deploy robust machine learning models, deep learning architectures, and data pipelines. • Lead the adoption of cutting-edge AI and data science technologies, including cloud-based platforms and big data tools. • Continuously explore new methodologies, tools, and approaches to improve the performance and scalability of AI solutions. • Strong programming skills in Python, R, or Scala, with expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch), data science tools (e.g., Pandas, Scikit-learn) and Natural Language Processing techniques. • Expertise in AG, Deep understanding of transformers, Diffusion models, Embeddings, Generative AI, multimodal, Agentive workflow, multi agent sync, Encoders, Decoders. • Proficiency in data preprocessing, feature engineering, model selection, and hyperparameter tuning. • Deep understanding of statistical analysis, machine learning, deep learning, and data mining techniques. • Excellent problem-solving skills, with the ability to think out of the box, strategically and creatively. • Strong communication skills with the ability to articulate complex technical concepts to non-technical audiences. • Establish best practices for data governance, data quality, and data management within the organization. • Ensure compliance with data privacy regulations and ethical standards in all AI and data science projects. • Collaborate with IT and data teams to maintain and enhance the organization’s data infrastructure. • Develop and maintain metrics to track the performance and impact of AI and data science initiatives. • Present findings, insights, and recommendations to senior leadership in a clear and concise manner. • Ensure continuous improvement of AI models and data analytics processes through regular review and feedback loops.

Signals

Skill ml-ops-engineer
0.27
Alias
KRA ai-engineer
0.60

Post-classification

Centroidupdated · n=1
Alias collision log
New-role queue
New skills captured26
New KRA capturedyes

Captured for admin review

Pandas primary LLM / GenAI Engineer pending
Natural Language Processing primary LLM / GenAI Engineer pending
Deep Learning primary LLM / GenAI Engineer pending
Data Pipelines primary LLM / GenAI Engineer pending
Cloud Platforms LLM / GenAI Engineer pending
Big Data LLM / GenAI Engineer pending
Data Preprocessing primary LLM / GenAI Engineer pending
Feature Engineering primary LLM / GenAI Engineer pending
Model Selection primary LLM / GenAI Engineer pending
Hyperparameter Tuning primary LLM / GenAI Engineer pending
Statistical Analysis primary LLM / GenAI Engineer pending
Data Mining primary LLM / GenAI Engineer pending
Data Governance primary LLM / GenAI Engineer pending
Data Quality primary LLM / GenAI Engineer pending
Data Management primary LLM / GenAI Engineer pending
Data Privacy Regulations primary LLM / GenAI Engineer pending
Ethical Standards primary LLM / GenAI Engineer pending
Data Infrastructure primary LLM / GenAI Engineer pending
Generative AI primary LLM / GenAI Engineer pending
Transformers primary LLM / GenAI Engineer pending
Diffusion Models primary LLM / GenAI Engineer pending
Multimodal primary LLM / GenAI Engineer pending
Agentive Workflow primary LLM / GenAI Engineer pending
Multi-Agent Sync primary LLM / GenAI Engineer pending
Encoders primary LLM / GenAI Engineer pending
Decoders primary LLM / GenAI Engineer pending
R&R fragment (sim 0.00) LLM / GenAI Engineer pending

• Lead the design and implementation of AI and data science strategies aligned with the company’s business objectives. • Identify opportunities for AI-driven innovations and data-driven insights acros…

Status: completed Created: 2026-05-27T13:48:05.927951Z Updated: 2026-05-27T13:53:40.506133Z API 3 duration: 36030 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

LLM / GenAI Engineer

domain · AI / ML CASE DOMAIN

slug: llm-genai-engineer · id: 151 · source: db

Domain=AI / ML; The JD centers on leading generative AI, transformers, embeddings, multimodal systems, and agentic workflows, which best matches LLM/GenAI engineering rather than general ML or data science.

Matched skills

PythonRScalaTensorFlowPyTorchPandasScikit-learnNatural Language ProcessingtransformersDiffusion modelsEmbeddingsGenerative AImultimodalAgentive workflowmulti agent sync

Matched dimensions

AI strategy and innovation leadershipEnd-to-end AI project lifecycle managementGenerative AI model developmentAdvanced analytics and predictive modelingData pipeline and platform developmentData governance and responsible AIPerformance measurement and continuous improvementExecutive communication and stakeholder alignment

Matched KRAs

Lead the design and implementation of AI and data science strategiesManage the end-to-end lifecycle of AI and data science projectsOversee the development of predictive models and machine learning algorithmsDesign and deploy robust machine learning models, deep learning architectures, and data pipelinesLead the adoption of cutting-edge AI and data science technologiesEstablish best practices for data governance, data quality, and data managementEnsure compliance with data privacy regulations and ethical standardsDevelop and maintain metrics to track performance and impactPresent findings, insights, and recommendations to senior leadership

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

Roles & Responsibilities


AI & Data Strategy Development:


• Lead the design and implementation of AI and data science strategies aligned with the company’s business objectives.
• Identify opportunities for AI-driven innovations and data-driven insights across the organization.
• Collaborate with senior management to align AI and data initiatives with long-term business goals.


Project Leadership & Execution:


• Manage the end-to-end lifecycle of AI and data science projects, from conceptualization to deployment.
• Oversee the development of predictive models, machine learning algorithms, and advanced analytics solutions.
• Ensure that all AI and data science projects meet quality, accuracy, and scalability standards.


Technical Expertise & Innovation:


• Design and deploy robust machine learning models, deep learning architectures, and data pipelines.
• Lead the adoption of cutting-edge AI and data science technologies, including cloud-based platforms and big data tools.
• Continuously explore new methodologies, tools, and approaches to improve the performance and scalability of AI solutions.
• Strong programming skills in Python, R, or Scala, with expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch), data science tools (e.g., Pandas, Scikit-learn) and Natural Language Processing techniques.
• Expertise in AG, Deep understanding of transformers, Diffusion models, Embeddings, Generative AI, multimodal, Agentive workflow, multi agent sync, Encoders, Decoders.
• Proficiency in data preprocessing, feature engineering, model selection, and hyperparameter tuning.
• Deep understanding of statistical analysis, machine learning, deep learning, and data mining techniques.
• Excellent problem-solving skills, with the ability to think out of the box, strategically and creatively.
• Strong communication skills with the ability to articulate complex technical concepts to non-technical audiences.


Data Governance & Quality Management:


• Establish best practices for data governance, data quality, and data management within the organization.
• Ensure compliance with data privacy regulations and ethical standards in all AI and data science projects.
• Collaborate with IT and data teams to maintain and enhance the organization’s data infrastructure.


Performance Monitoring & Reporting:


• Develop and maintain metrics to track the performance and impact of AI and data science initiatives.
• Present findings, insights, and recommendations to senior leadership in a clear and concise manner.
• Ensure continuous improvement of AI models and data analytics processes through regular review and feedback loops.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
R Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: R id=194 · r

Aliases — catalog

  • R (VERSION)
  • R 3 (VERSION)
  • R 3.x (VERSION)
  • R 4 (VERSION)
  • R 4.0 (VERSION)
  • R 4.1 (VERSION)
  • R 4.2 (VERSION)
  • R 4.3 (VERSION)
  • R 4.4 (VERSION)
  • R 4.x (VERSION)

Context tags (catalog)

Bioconductor CRAN R Markdown Shiny Tidyverse caret data.table dplyr ggplot2 glm lme4 lubridate rstan tidyr tidyverse

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
R Core Team
License
gpl_v2
Year introduced
1993
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
R 4.x

Maturity reasoning: R appears in many data science, statistics, and analytics job postings, and CRAN remains active with broad package usage across academia and industry.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Scala Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Scala id=102 · scala

Aliases — catalog

  • Scala (CANONICAL) primary

Context tags (catalog)

Akka Apache Kafka Cats Flink JVM Monads Play Framework SBT ScalaTest Shapeless Spark Spark SQL ZIO case class for-comprehension functional programming implicit pattern matching typeclass

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
EPFL
License
apache_2
Year introduced
2004
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Scala still appears in many backend/data engineering JDs, especially with Spark and Akka, and remains supported by major JVM ecosystems; it’s not a sunset technology.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TensorFlow Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: TensorFlow id=196 · tensorflow

Aliases — catalog

  • TensorFlow (CANONICAL) primary
  • TF1 (VERSION)
  • TF2 (VERSION)
  • TensorFlow 1 (VERSION)
  • TensorFlow 1.x (VERSION)
  • TensorFlow 2 (VERSION)
  • TensorFlow 2.x (VERSION)
  • tensorflow 1 (VERSION)
  • tensorflow 1.x (VERSION)
  • tensorflow 2 (VERSION)
  • tensorflow 2.x (VERSION)
  • tensorflow v1 (VERSION)
  • tensorflow v2 (VERSION)
  • tf (VERSION)
  • tf1 (VERSION)
  • tf2 (VERSION)

Context tags (catalog)

AutoGraph Distributed Training Eager Execution Estimator GPU Gradient Descent Hyperparameter Tuning Keras ModelCheckpoint Neural Networks ONNX SavedModel TF Lite TF Serving TF.js TFX TPU TensorBoard TensorFlow Hub TensorFlow Lite TensorFlow Serving Transfer Learning XLA tf.data tf.keras

Stored enrichment (catalog DB)

Category
Library
Sub-category
Machine Learning Library
Vendor
Google
License
apache_2
Year introduced
2015
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
2.x

Maturity reasoning: TensorFlow appears in many ML/AI job descriptions and remains a standard production framework, with strong GitHub activity and broad vendor support from Google and cloud platforms.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
156
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PyTorch Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PyTorch id=195 · pytorch

Aliases — catalog

  • PyTorch (CANONICAL) primary

Context tags (catalog)

CUDA DataLoader GPU GPU acceleration Hugging Face Lightning ONNX PyTorch Lightning ReLU Tensor TorchScript autograd backpropagation checkpointing deep learning distributed training loss functions mixed precision model training neural networks nn.Module optimizers tensor torchaudio torchscript torchvision transfer learning

Stored enrichment (catalog DB)

Category
Library
Sub-category
Machine Learning Library
Vendor
Meta
License
bsd
Year introduced
2016
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: PyTorch appears in a large volume of ML/AI job descriptions and is a standard framework in research and production, alongside TensorFlow and CUDA ecosystems.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
156
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Model Fine-Tuning & Adaptation Catalog dimension db id 212

    Library dimension (catalog)

    Roles linked in library: AI Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pandas Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Scikit-learn Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: scikit-learn id=197 · scikit-learn

Aliases — catalog

  • scikit-learn (CANONICAL) primary

Context tags (catalog)

GridSearchCV K-fold NumPy Pandas Pipeline SVM classification clustering cross-validation cross_validation data_preprocessing ensemble_methods feature engineering feature_importance hyperparameter_tuning imbalanced-learn joblib logistic regression metrics model_selection pipelines predictive_modeling preprocessing random forest regression scoring_metrics train_test_split

Stored enrichment (catalog DB)

Category
Library
Sub-category
Machine Learning Library
Vendor
scikit-learn developers
License
bsd
Year introduced
2007
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in ML/data science job descriptions and widely used in production Python ML stacks; no vendor sunset or replacement signal, and GitHub activity remains strong.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
156
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Natural Language Processing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deep Learning Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Pipelines Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Cloud Platforms Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Big Data Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Preprocessing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Feature Engineering Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Model Selection Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Hyperparameter Tuning Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Statistical Analysis Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Mining Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Governance Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Quality Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Management Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Privacy Regulations Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Ethical Standards Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Infrastructure Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Metrics Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: metrics id=1625 · metrics

Aliases — catalog

  • metrics (CANONICAL) primary
  • Metrics (CANONICAL)

Context tags (catalog)

A/B testing KPI KPIs SLI SLIs SLO SLOs alerting analytics benchmarking business intelligence dashboard dashboards data analysis data collection data visualization data-driven data-driven decisions event tracking log aggregation log analysis logging measurement frameworks metric collection metrics aggregation metrics-driven culture monitoring observability performance indicators real-time real-time data real-time metrics reporting reporting tools scorecards statistical methods tracing trend analysis user engagement

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Metrics
Confidence
0.84
Version strategy
NOT_APPLICABLE

Maturity reasoning: Metrics is a standard observability requirement and appears in many JDs for monitoring/telemetry roles; vendors like Prometheus/Grafana and cloud platforms center it in their docs and product stacks.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
3506
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Backend Observability, Logging, and Diagnostics Catalog dimension db id 388

    Library dimension (catalog)

    Roles linked in library: Kotlin Backend Developer, Scala Backend Developer

  • Experiment Tracking and Evaluation Catalog dimension db id 44

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Observability and Diagnostics Catalog dimension db id 287

    Library dimension (catalog)

    Roles linked in library: Go Backend Developer, Java Backend Developer, Python Backend Developer

  • Observability and Incident Response Catalog dimension db id 10

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Node.js Backend Developer, PHP Backend Developer

  • Observability and Operations Catalog dimension db id 143

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Experiment Tracking and Evaluation
experiment-tracking-and-evaluation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Diagnostics
observability-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Generative AI Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Transformers Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Diffusion Models Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Embeddings Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Embeddings id=1195 · embeddings

Aliases — catalog

  • Embeddings (CANONICAL)

Context tags (catalog)

BERT GloVe contextual embeddings deep learning dimensionality reduction fastText feature extraction natural language processing nearest neighbors semantic similarity sentence embeddings transfer learning transformers vector space word embeddings

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Vector Representation
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Embeddings are a standard ML concept and appear widely in JDs for search, recommendation, and LLM/RAG roles; major vendors like OpenAI, Cohere, and AWS expose embedding APIs, signaling broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
905
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Multimodal Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Agentive Workflow Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Multi-Agent Sync Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Encoders Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Decoders Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Other
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
R in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Scala in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Scala in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TensorFlow in_db
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PyTorch in_db
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
PyTorch in_db
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Scikit-learn in_db
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Metrics in_db
Backend Observability, Logging, and Diagnostics
backend-observability-logging-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Metrics in_db
Experiment Tracking and Evaluation
experiment-tracking-and-evaluation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Metrics in_db
Observability and Diagnostics
observability-and-diagnostics
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Metrics in_db
Observability and Incident Response
observability-and-incident-response
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Metrics in_db
Observability and Operations
observability-and-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Embeddings in_db
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Embeddings in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Embeddings in_db
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Pandas | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Natural Language Processing | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Deep Learning | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Pipelines | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Cloud Platforms | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Big Data | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Preprocessing | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Feature Engineering | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Model Selection | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Hyperparameter Tuning | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Statistical Analysis | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Mining | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Governance | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Quality | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Management | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Privacy Regulations | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Ethical Standards | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Data Infrastructure | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Generative AI | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Transformers | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Diffusion Models | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Multimodal | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Agentive Workflow | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Multi-Agent Sync | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Encoders | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Decoders | type=Other subtype=general nature=TOOL lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
DomainOther
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
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      "domain": "Other"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": null,
    "raw": null
  },
  "job_locations": [],
  "role": null,
  "role_aliases": [],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 3,
      "heading": "AI \u0026 Data Strategy Development",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Lead the design and",
        "last_5_words": "with long-term business goals."
      },
      "text": "\u2022 Lead the design and implementation of AI and data science strategies aligned with the company\u2019s business objectives.\n\u2022 Identify opportunities for AI-driven innovations and data-driven insights across the organization.\n\u2022 Collaborate with senior management to align AI and data initiatives with long-term business goals.",
      "word_count": 45
    },
    {
      "bullet_count": 3,
      "heading": "Project Leadership \u0026 Execution",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Manage the end-to-end",
        "last_5_words": "quality, accuracy, and scalability standards."
      },
      "text": "\u2022 Manage the end-to-end lifecycle of AI and data science projects, from conceptualization to deployment.\n\u2022 Oversee the development of predictive models, machine learning algorithms, and advanced analytics solutions.\n\u2022 Ensure that all AI and data science projects meet quality, accuracy, and scalability standards.",
      "word_count": 45
    },
    {
      "bullet_count": 9,
      "heading": "Technical Expertise \u0026 Innovation",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Design and deploy robust",
        "last_5_words": "to non-technical audiences."
      },
      "text": "\u2022 Design and deploy robust machine learning models, deep learning architectures, and data pipelines.\n\u2022 Lead the adoption of cutting-edge AI and data science technologies, including cloud-based platforms and big data tools.\n\u2022 Continuously explore new methodologies, tools, and approaches to improve the performance and scalability of AI solutions.\n\u2022 Strong programming skills in Python, R, or Scala, with expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch), data science tools (e.g., Pandas, Scikit-learn) and Natural Language Processing techniques.\n\u2022 Expertise in AG, Deep understanding of transformers, Diffusion models, Embeddings, Generative AI, multimodal, Agentive workflow, multi agent sync, Encoders, Decoders.\n\u2022 Proficiency in data preprocessing, feature engineering, model selection, and hyperparameter tuning.\n\u2022 Deep understanding of statistical analysis, machine learning, deep learning, and data mining techniques.\n\u2022 Excellent problem-solving skills, with the ability to think out of the box, strategically and creatively.\n\u2022 Strong communication skills with the ability to articulate complex technical concepts to non-technical audiences.",
      "word_count": 174
    },
    {
      "bullet_count": 3,
      "heading": "Data Governance \u0026 Quality Management",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Establish best practices for",
        "last_5_words": "the organization\u2019s data infrastructure."
      },
      "text": "\u2022 Establish best practices for data governance, data quality, and data management within the organization.\n\u2022 Ensure compliance with data privacy regulations and ethical standards in all AI and data science projects.\n\u2022 Collaborate with IT and data teams to maintain and enhance the organization\u2019s data infrastructure.",
      "word_count": 45
    },
    {
      "bullet_count": 3,
      "heading": "Performance Monitoring \u0026 Reporting",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Develop and maintain metrics",
        "last_5_words": "review and feedback loops."
      },
      "text": "\u2022 Develop and maintain metrics to track the performance and impact of AI and data science initiatives.\n\u2022 Present findings, insights, and recommendations to senior leadership in a clear and concise manner.\n\u2022 Ensure continuous improvement of AI models and data analytics processes through regular review and feedback loops.",
      "word_count": 45
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "R"
    },
    {
      "is_primary": true,
      "skill_name": "Scala"
    },
    {
      "is_primary": true,
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    },
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    },
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    {
      "is_primary": true,
      "skill_name": "Data Pipelines"
    },
    {
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      "skill_name": "Cloud Platforms"
    },
    {
      "is_primary": false,
      "skill_name": "Big Data"
    },
    {
      "is_primary": true,
      "skill_name": "Data Preprocessing"
    },
    {
      "is_primary": true,
      "skill_name": "Feature Engineering"
    },
    {
      "is_primary": true,
      "skill_name": "Model Selection"
    },
    {
      "is_primary": true,
      "skill_name": "Hyperparameter Tuning"
    },
    {
      "is_primary": true,
      "skill_name": "Statistical Analysis"
    },
    {
      "is_primary": true,
      "skill_name": "Data Mining"
    },
    {
      "is_primary": true,
      "skill_name": "Data Governance"
    },
    {
      "is_primary": true,
      "skill_name": "Data Quality"
    },
    {
      "is_primary": true,
      "skill_name": "Data Management"
    },
    {
      "is_primary": true,
      "skill_name": "Data Privacy Regulations"
    },
    {
      "is_primary": true,
      "skill_name": "Ethical Standards"
    },
    {
      "is_primary": true,
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    },
    {
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    },
    {
      "is_primary": true,
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    },
    {
      "is_primary": true,
      "skill_name": "Transformers"
    },
    {
      "is_primary": true,
      "skill_name": "Diffusion Models"
    },
    {
      "is_primary": true,
      "skill_name": "Embeddings"
    },
    {
      "is_primary": true,
      "skill_name": "Multimodal"
    },
    {
      "is_primary": true,
      "skill_name": "Agentive Workflow"
    },
    {
      "is_primary": true,
      "skill_name": "Multi-Agent Sync"
    },
    {
      "is_primary": true,
      "skill_name": "Encoders"
    },
    {
      "is_primary": true,
      "skill_name": "Decoders"
    }
  ],
  "jd_role": null,
  "nano_parsed": {
    "JD_type": "pass",
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    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 3,
        "heading": "AI \u0026 Data Strategy Development",
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        "source_marker": {
          "first_5_words": "\u2022 Lead the design and",
          "last_5_words": "with long-term business goals."
        },
        "text": "\u2022 Lead the design and implementation of AI and data science strategies aligned with the company\u2019s business objectives.\n\u2022 Identify opportunities for AI-driven innovations and data-driven insights across the organization.\n\u2022 Collaborate with senior management to align AI and data initiatives with long-term business goals.",
        "word_count": 45
      },
      {
        "bullet_count": 3,
        "heading": "Project Leadership \u0026 Execution",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Manage the end-to-end",
          "last_5_words": "quality, accuracy, and scalability standards."
        },
        "text": "\u2022 Manage the end-to-end lifecycle of AI and data science projects, from conceptualization to deployment.\n\u2022 Oversee the development of predictive models, machine learning algorithms, and advanced analytics solutions.\n\u2022 Ensure that all AI and data science projects meet quality, accuracy, and scalability standards.",
        "word_count": 45
      },
      {
        "bullet_count": 9,
        "heading": "Technical Expertise \u0026 Innovation",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Design and deploy robust",
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}
API 2 — extract-details
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        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "metrics",
        "sub_category_id": 3506,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Backend Observability, Logging, and Diagnostics",
            "id": 388,
            "rationale": "Instrumentation and troubleshooting practices used to understand and improve backend service behavior in production and lower environments. This includes logs, metrics, traces, alerting, dashboards, structured logging, distributed tracing, health checks, and root-cause analysis using ecosystem-specific tools such as SLF4J, Logback, Micrometer, OpenTelemetry, Prometheus, Grafana, ILogger, Serilog, and Application Insights.",
            "slug": "backend-observability-logging-and-diagnostics",
            "source": "db"
          },
          "input_skill": "Metrics",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Experiment Tracking and Evaluation",
            "id": 44,
            "rationale": "Tools and practices for recording experiments, comparing runs, and assessing model quality before release. This dimension focuses on reproducibility, metrics, artifacts, and offline evaluation workflows.",
            "slug": "experiment-tracking-and-evaluation",
            "source": "db"
          },
          "input_skill": "Metrics",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Diagnostics",
            "id": 287,
            "rationale": "Instrumentation and troubleshooting practices used to understand Java service behavior in production and lower environments. This cluster covers logs, metrics, traces, correlation IDs, and root-cause analysis from service telemetry.",
            "slug": "observability-and-diagnostics",
            "source": "db"
          },
          "input_skill": "Metrics",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Incident Response",
            "id": 10,
            "rationale": "Instrumentation and production troubleshooting practices used to keep backend services reliable. Includes logs, metrics, traces, alerting, dashboards, and incident diagnosis.",
            "slug": "observability-and-incident-response",
            "source": "db"
          },
          "input_skill": "Metrics",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Observability and Operations",
            "id": 143,
            "rationale": "Monitoring, logging, tracing, and operational readiness patterns used to keep cloud platforms supportable. Cloud Architects use this to define what telemetry and operational controls workloads must expose.",
            "slug": "observability-and-operations",
            "source": "db"
          },
          "input_skill": "Metrics",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Metrics",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Generative AI",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "generative-ai",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Transformers",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "transformers",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Diffusion Models",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "diffusion-models",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Embeddings",
          "alias_type": "CANONICAL",
          "id": 1831,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Embeddings",
        "id": 1195,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "embeddings",
        "sub_category_id": 905,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "ML Frameworks and Libraries",
            "id": 40,
            "rationale": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
            "slug": "ml-frameworks-and-libraries",
            "source": "db"
          },
          "input_skill": "Embeddings",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Embeddings",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Systems Programming",
            "id": 166,
            "rationale": "Systems programming covers low-level software development where performance, memory safety, and direct control over resources matter. Rust fits here because it is commonly used for OS-adjacent services, infrastructure components, and other performance-sensitive systems code.",
            "slug": "d_init_02",
            "source": "db"
          },
          "input_skill": "Embeddings",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Embeddings",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Multimodal",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "multimodal",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Agentive Workflow",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "agentive-workflow",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Multi-Agent Sync",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "multi-agent-sync",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Encoders",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "encoders",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Decoders",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Other",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "decoders",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Pandas",
    "Natural Language Processing",
    "Deep Learning",
    "Data Pipelines",
    "Cloud Platforms",
    "Big Data",
    "Data Preprocessing",
    "Feature Engineering",
    "Model Selection",
    "Hyperparameter Tuning",
    "Statistical Analysis",
    "Data Mining",
    "Data Governance",
    "Data Quality",
    "Data Management",
    "Data Privacy Regulations",
    "Ethical Standards",
    "Data Infrastructure",
    "Generative AI",
    "Transformers",
    "Diffusion Models",
    "Multimodal",
    "Agentive Workflow",
    "Multi-Agent Sync",
    "Encoders",
    "Decoders"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "LLM / GenAI Engineer",
    "id": 151,
    "rationale": "Domain=AI / ML; The JD centers on leading generative AI, transformers, embeddings, multimodal systems, and agentic workflows, which best matches LLM/GenAI engineering rather than general ML or data science.",
    "role_archetype": null,
    "slug": "llm-genai-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "R",
      "tag": "in_db"
    },
    {
      "skill": "Scala",
      "tag": "in_db"
    },
    {
      "skill": "TensorFlow",
      "tag": "in_db"
    },
    {
      "skill": "PyTorch",
      "tag": "in_db"
    },
    {
      "skill": "Pandas",
      "tag": "new"
    },
    {
      "skill": "Scikit-learn",
      "tag": "in_db"
    },
    {
      "skill": "Natural Language Processing",
      "tag": "new"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Deep Learning",
      "tag": "new"
    },
    {
      "skill": "Data Pipelines",
      "tag": "new"
    },
    {
      "skill": "Cloud Platforms",
      "tag": "new"
    },
    {
      "skill": "Big Data",
      "tag": "new"
    },
    {
      "skill": "Data Preprocessing",
      "tag": "new"
    },
    {
      "skill": "Feature Engineering",
      "tag": "new"
    },
    {
      "skill": "Model Selection",
      "tag": "new"
    },
    {
      "skill": "Hyperparameter Tuning",
      "tag": "new"
    },
    {
      "skill": "Statistical Analysis",
      "tag": "new"
    },
    {
      "skill": "Data Mining",
      "tag": "new"
    },
    {
      "skill": "Data Governance",
      "tag": "new"
    },
    {
      "skill": "Data Quality",
      "tag": "new"
    },
    {
      "skill": "Data Management",
      "tag": "new"
    },
    {
      "skill": "Data Privacy Regulations",
      "tag": "new"
    },
    {
      "skill": "Ethical Standards",
      "tag": "new"
    },
    {
      "skill": "Data Infrastructure",
      "tag": "new"
    },
    {
      "skill": "Metrics",
      "tag": "in_db"
    },
    {
      "skill": "Generative AI",
      "tag": "new"
    },
    {
      "skill": "Transformers",
      "tag": "new"
    },
    {
      "skill": "Diffusion Models",
      "tag": "new"
    },
    {
      "skill": "Embeddings",
      "tag": "in_db"
    },
    {
      "skill": "Multimodal",
      "tag": "new"
    },
    {
      "skill": "Agentive Workflow",
      "tag": "new"
    },
    {
      "skill": "Multi-Agent Sync",
      "tag": "new"
    },
    {
      "skill": "Encoders",
      "tag": "new"
    },
    {
      "skill": "Decoders",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
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            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1195,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Embeddings",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1195,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 151,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Systems Programming",
          "id": 166,
          "rationale": "Systems programming covers low-level software development where performance, memory safety, and direct control over resources matter. Rust fits here because it is commonly used for OS-adjacent services, infrastructure components, and other performance-sensitive systems code.",
          "slug": "d_init_02",
          "source": "db"
        },
        "dimension_id": 166,
        "input_skill": "Embeddings",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1195,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "92062496-bbec-43d1-b056-6b5e6b0f5235"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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